The field of artificial intelligence has long been dominated by the scaling of models and data. However, recent discussions suggest that internal coherence might play a significant role in how AI systems develop and perform.
Internal coherence refers to the consistency and harmony within an AI system's components, which could influence its effectiveness and scalability. This concept challenges the traditional focus solely on size and complexity.
Understanding and optimizing internal coherence could lead to more robust AI systems, potentially reshaping the landscape of machine learning and its applications.